As knowledge of tumors including cancers evolve, it becomes more clear that they are extraordinarily heterogeneous and multifactorial. Tumors and cancers have a wide range of genotypes and phenotypes, they are influenced by their individualized cell receptors (or lack thereof), micro-environment, extracellular matrix, tumor vascularization, neighboring immune cells, and accumulations of mutations, with differing capacities for proliferation, migration, stem cell properties and invasion. This scope of heterogeneity exists even among same classes of tumors. See generally: Nature Insight: Tumor Heterogeneity (entire issue of articles), 19 Sep. 2013 (Vol. 501, Issue 7467); Zellmer and Zhang, “Evolving concepts of tumor heterogeneity”, Cell and Bioscience 2014, 4:69.
Traditionally, physicians have treated tumors, including cancers, as the same within class type (including within receptor type) without taking into account the enormous fundamental individualized nature of the diseased tissue. Patients have been treated with available chemotherapeutic agents based on class and receptor type, and if they do not respond, they are treated with an alternative therapeutic, if it exists. This is an empirical approach to medicine.
There has been a growing trend toward taking into account the heterogeneity of tumors at a more fundamental level as a means to create individualized therapies, however, this trend is still in its formative stages. What is desperately needed are approaches to obtain more metadata about the tumor to inform therapeutic treatment in a manner that allows the prescription of approaches more closely tailored to the individual tumor, and perhaps more importantly, avoiding therapies destined to fail and waste valuable time, which can be life-determinative.
A number of companies and institutions are active in the area of classical, and some more advanced, genetic testing, diagnostics, and predictions for the development of human diseases, including, for example: Affymetrix, Inc.; Bio-Rad, Inc; Roche Diagnostics; Genomic Health, Inc.; Regents of the University of California; Illumina; Fluidigm Corporation; Sequenom, Inc.; High Throughput Genomics; NanoString Technologies; Thermo Fisher; Danaher; Becton, Dickinson and Company; bioMerieux; Johnson & Johnson, Myriad Genetics, and Hologic.
Several companies have developed technology or products directed to gene expression profiling and disease classification. For example, Genomic Health, Inc. is the assignee of numerous patents pertaining to gene expression profiling, for example: U.S. Pat. Nos. 7,081,340; 8,808,994; 8,034,565; 8,206,919; 7,858,304; 8,741,605; 8,765,383; 7,838,224; 8,071,286; 8,148,076; 8,008,003; 8,725,426; 7,888,019; 8,906,625; 8,703,736; 7,695,913; 7,569,345; 8,067,178; 7,056,674; 8,153,379; 8,153,380; 8,153,378; 8,026,060; 8,029,995; 8,198,024; 8,273,537; 8,632,980; 7,723,033; 8,367,345; 8,911,940; 7,939,261; 7,526,637; 8,868,352; 7,930,104; 7,816,084; 7,754,431 and 7,208,470, and their foreign counterparts.
U.S. Pat. No. 9,076,104 to the Regents of the University of California titled “Systems and Methods for Identifying Drug Targets using Biological Networks” claims a method with computer executable instructions by a processor for predicting gene expression profile changes on inhibition of proteins or genes of drug targets on treating a disease, that includes constructing a genetic network using a dynamic Bayesian network based at least in part on knowledge of drug inhibiting effects on a disease, associating a set of parameters with the constructed dynamic Bayesian network, determining the values of a joint probability distribution via an automatic procedure, deriving a mean dynamic Bayesian network with averaged parameters and calculating a quantitative prediction based at least in part on the mean dynamic Bayesian network, wherein the method searches for an optimal combination of drug targets whose perturbed gene expression profiles are most similar to healthy cells.
Affymetrix has developed a number of products related to gene expression profiling. Non-limiting examples of U.S. patents to Affymetrix include: U.S. Pat. Nos. 6,884,578; 8,029,997; 6,308,170; 6,720,149; 5,874,219; 6,171,798; and 6,391,550.
Likewise, Bio-Rad has a number of products directed to gene expression profiling. Illustrative examples of U.S. patents to Bio-Rad include: U.S. Pat. Nos. 8,021,894; 8,451,450; 8,518,639; 6,004,761; 6,146,897; 7,299,134; 7,160,734; 6,675,104; 6,844,165; 6,225,047; 7,754,861 and 6,004,761.
Koninklijke Philips N.V. (NL) has filed a number of patent applications in the general area of assessment of cellular signaling pathway activity using various mathematical models, including U.S. Ser. No. 14/233,546 (WO 2013/011479), titled “Assessment of Cellular Signaling Pathway Using Probabilistic Modeling of Target Gene Expression”; U.S. Ser. No. 14/652,805 (WO 2014/102668) titled “Assessment of Cellular Signaling Pathway Activity Using Linear Combinations of Target Gene Expressions; WO 2014/174003 titled “Medical Prognosis and Prediction of Treatment Response Using Multiple Cellular Signaling Pathway Activities; and WO 2015/101635 titled “Assessment of the PI3K Cellular Signaling Pathway Activity Using Mathematical Modeling of Target Gene Expression.
Despite this progress, more work is needed to definitively characterize tumor cellular behavior. In particular, there is a critical need to determine which pathways have become pathogenic to the cell. However, it is difficult to identify and separate abnormal cellular signaling from normal cellular pathway activity.
Transforming growth factor-β (TGF-β) is a cytokine that controls various functions in many cell types in humans, such as proliferation, differentiation, and wound healing. In pathological disorders, such as cancer (e.g., colon, breast, prostate), the TGF-β cellular signaling pathway can play two opposing roles, either as a tumor suppressor or as a tumor promoter. TGF-β may act as a tumor suppressor in the early phases of cancer development, however in more progressed cancerous tissue TGF-β can act as a tumor promoter by acting as a regulator of invasion and metastasis (see Padua D. and Massague J., “Roles of TGF-β in metastasis”, Cell Research, Vol. 19, No. 1, 2009, pages 89 to 102).
TGF-β exists in three isoforms (gene names: TGF-β1, TGF-β2, TGF-β3). It is secreted as an inactive precursor homodimeric protein, which is known to be increased in cancer cells compared to their normal counterparts (see Massague J., “How cells read TGF-β signals”, Nature Reviews Molecular Cell Biology, Vol. 1, No. 3, 2000, pages 169 to 178).
The TGF-β precursor can be proteolytically activated, after which it binds to an extracellular TGF-β receptor that initiates an intracellular “SMAD” signaling pathway. Various SMAD proteins (receptor-regulated or R-SMADs (SMAD 1, 2, 3, 5 and 8) and SMAD4) form a heterocomplex that enters the nucleus where it acts as a transcription factor, inducing the expression of a range of proteins which affect tumor growth (see FIG. 1; L. TGF-β=Latent TGF-β; PR=Proteasome; PH=Phosphatase; Co—R═Co-repressors; Co-A=Co-activators). The term “TGF-β cellular signaling pathway” herein refers to a signaling process triggered by TGF-β binding to the extracellular TGF receptor causing the intracellular SMAD cascade, which ultimately leads to the formation of a SMAD complex that acts as a transcription factor.
A number of anti-TGF-β therapies are in preclinical or clinical development (see Yingling J. M. et al., “Development of TGF-β signaling inhibitors for cancer therapy”, Nature Reviews Drug Discovery, Vol. 3, No. 12, 2004, pages 1011 to 1022; Nacif and Shaker, “Targeting Transforming Growth Factor-B (TGF-β) in Cancer and Non-Neoplastic Diseases”; Journal of Cancer Therapy, 2014, 5, 735-747).
However, physicians must use caution in administering an anti-TGF-β drug to a patient with a tumor, including cancer, because in some tumors, TGF-β is playing a tumor suppressing role. It is therefore important to be able to more accurately assess the functional state of the TGF-β cellular signaling pathway at specific points in disease progression. For example, the TGF-β cellular signaling pathway, with respect to cancer, is more likely to be tumor-promoting in its active state and tumor-suppressing in its passive state. Notwithstanding, it can be difficult to discern the difference in a diseased cell.
It is therefore an object of the invention to provide a more accurate process to determine the tumorigenic propensity of the TGF-β cellular signaling pathway in a cell, as well as associated methods of therapeutic treatment, kits, systems, etc.